Global asymptotic stability of fractional-order competitive neural networks with multiple time-varying-delay links

Yao Xu, Jintong Yu, Wenxue Li and Jiqiang Feng

Applied Mathematics and Computation, 2021, vol. 389, issue C

Abstract: Competitive neural networks have become increasingly popular since this kind of neural networks can better describe the dynamics of cortical cognitive maps with unsupervised synaptic modifications. In this paper, we first propose fractional-order competitive neural networks with multiple time-varying-delay links and explore the global asymptotic stability of this class of neural networks. A novel and generalized integral inequality related to every upper bound of each time-varying delay is given. Moreover, based on Lyapunov method and graph theory, we obtain some sufficient conditions with the help of this integral inequality to guarantee the global asymptotic stability. The theoretical results offer a new perspective to show the close relationship between the stability criterion and the topological structure of networks. Finally, an illustrative numerical example is given to demonstrate the feasibility and effectiveness of the theoretical results.

Keywords: Competitive neural networks; Fractional derivatives; Global asymptotic stability; Time-varying delays; Multiple links (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:389:y:2021:i:c:s0096300320304562

DOI: 10.1016/j.amc.2020.125498

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